Episode 36 — OWASP GenAI/LLM Top 10
The OWASP Top 10 is one of the most widely recognized awareness frameworks in application security, and its adaptation to generative AI and large language models reflects the growing importance of securing these new systems. Its purpose is to identify and describe the most critical security risks, distilling a complex landscape into a prioritized list that organizations can act upon. For AI, the OWASP GenAI/LLM Top 10 highlights risks that go beyond traditional software concerns, accounting for the unique behaviors of models and the ways they are used in practice. By mapping these threats into a common framework, OWASP provides a foundation for prioritization: teams know where to focus resources first. This structured guidance transforms AI security from a diffuse challenge into a manageable roadmap.
Risk one is prompt injection, the generative AI analogue to injection attacks in traditional software. Here, adversaries craft malicious inputs designed to override the model’s intended instructions or bypass safeguards. By embedding hidden directives in user prompts, attackers can steer models toward exposing sensitive information, performing unauthorized actions, or producing harmful content. Because models often treat prompts as trusted context, these injections can propagate through entire workflows. Prompt injection is systemic in nature: it undermines trust not only in the model but also in every system that consumes its outputs. For AI security teams, this risk demands attention at design, filtering, and monitoring levels, making it one of the most urgent concerns in the OWASP list.
Risk two is data leakage, a threat that arises from the intersection of sensitive training data, user prompts, and output behaviors. Models may inadvertently expose fragments of training records or reveal personal details when queried cleverly. Sensitive prompts entered by users may also be logged insecurely, leaving them accessible to unauthorized parties. Inadequate log protection or careless telemetry practices compound the risk, turning operational records into sources of leakage. Re-identification risks loom large: even anonymized data can sometimes be reconstructed when enough detail leaks across outputs. Protecting against data leakage requires encryption, strict logging policies, and careful governance over what models retain and disclose. This risk highlights how confidentiality must be considered at every point in the AI lifecycle.
Risk three is insecure output handling. Generative AI models produce text, code, or other content that may flow directly into downstream systems, creating risks if those outputs are not validated. For example, a model response could include malicious scripts that, if executed, compromise client applications. Without sanitization, unsafe instructions may slip into workflows that trust model outputs implicitly. The risk extends to unsafe execution paths, where model-generated code or queries are run automatically without containment. Insecure output handling turns model creativity into an attack vector, showing why outputs must be treated with the same caution as untrusted user input. Safe design requires filtering, sandboxing, and robust validation before outputs are integrated into sensitive systems.
Risk four is model denial of service, which mirrors traditional denial-of-service attacks but in AI contexts takes unique forms. Adversaries may overwhelm models with high volumes of requests, causing resource exhaustion that degrades availability. Denial-of-wallet scenarios occur when attackers deliberately generate expensive queries, draining financial resources by forcing costly computation. Long-context flooding takes advantage of models with extended memory, filling prompts with unnecessary content to waste capacity. Compute starvation results when attackers monopolize scarce processing power, preventing legitimate users from accessing services. These attacks highlight that AI systems are not only targets of manipulation but also valuable resources that can be denied. Defending against them requires quotas, rate limiting, and monitoring tuned to the economics of AI workloads.
Risk five is supply chain vulnerabilities, which affect the components and dependencies upon which AI systems rely. Poisoned pre-trained models may contain malicious weights or biases introduced upstream. Dependency risks in libraries expose pipelines to tampered packages or abandoned projects. Datasets themselves can be manipulated, embedding toxic or misleading content that later shapes outputs. Plugin exploitation adds another dimension, as third-party connectors may serve as hidden attack vectors. The complexity of AI supply chains magnifies these risks, as organizations often integrate external resources without full visibility into their provenance. This risk emphasizes the need for provenance tracking, artifact verification, and disciplined dependency management. In AI, the chain of trust is only as strong as its weakest link.
Risk six is insecure plugin interfaces, a growing concern as AI systems integrate more external tools and connectors. Plugins often require elevated permissions to perform useful tasks, but excessive privileges can open pathways for abuse. Unsafe external calls may allow adversaries to trigger harmful actions or leak sensitive information. Authentication gaps in plugin systems can permit unauthorized use, while weak authorization controls risk privilege escalation. Because plugins extend models into real-world environments, insecure interfaces transform abstract risks into tangible harm. Organizations must enforce strict permissioning, sandbox plugin execution, and monitor their activity continuously. This risk underscores that the boundary between models and the outside world is one of the most fragile—and must be guarded carefully.
Risk seven highlights inadequate monitoring, where the absence of telemetry pipelines or anomaly detection leaves organizations blind. Without sufficient logs, misuse attempts go unnoticed, and delayed responses become inevitable. Missing anomaly detection means that abusive patterns—such as repeated probing or denial-of-service—may persist undetected until damage is done. Failure to capture misuse events also undermines forensic investigation, preventing root-cause analysis and remediation. For AI, monitoring is more than an operational task; it is a security necessity. Telemetry ensures that unexpected or adversarial behaviors do not slip past unseen. Without it, risks cannot be managed because they cannot even be measured. This risk reinforces the importance of visibility as the foundation of AI security.
Risk eight is model theft, which involves adversaries extracting intellectual property through queries, stolen checkpoints, or surrogate model training. Query-based extraction methods exploit repeated interactions to approximate weights and replicate capabilities. Stolen checkpoints compromise proprietary assets directly, giving attackers access to years of investment in training. Surrogate models, built by querying an original system, capture behavior patterns that dilute competitive advantage. Beyond financial loss, model theft raises compliance concerns if stolen systems are repurposed for harmful applications. Protecting against this risk requires rate limiting, watermarking, and strict checkpoint management. For organizations, safeguarding models is not only a security requirement but also an economic imperative.
Risk nine deals with overreliance on output, where humans or systems place undue trust in model responses. Hallucinated information may be accepted as fact if grounding checks are absent. Automated systems that consume model outputs without validation can propagate errors at scale, from business workflows to critical infrastructure. Downstream error propagation turns a single incorrect response into a systemic fault. This risk illustrates that AI systems are not just technical components but socio-technical ones, where human trust and machine reliability intersect. Mitigation requires grounding responses, applying verification steps, and embedding humans-in-the-loop where necessary. Without these safeguards, organizations risk building brittle systems that fail quietly but catastrophically.
Risk ten is insufficient governance, the structural weakness that allows all other risks to flourish. Without defined policies, acceptable use rules, and accountability measures, AI systems operate without guardrails. Undefined responsibilities leave gaps where misuse can persist unchallenged. Missing audit trails prevent organizations from demonstrating compliance or responding effectively to incidents. Poor accountability undermines trust, both internally and externally. Insufficient governance is often invisible until it leads to visible failures—compliance breaches, reputational harm, or systemic misuse. By embedding governance, organizations ensure that the OWASP risks are not addressed piecemeal but within a coherent, enforceable framework. Governance transforms security from ad hoc reactions into structured assurance.
Mitigation alignment shows how the OWASP list maps directly to defensive measures. Prompt filtering addresses injection risks, data encryption protects against leakage, and sandboxing ensures that insecure outputs cannot compromise downstream systems. Telemetry dashboards provide the visibility needed to mitigate monitoring gaps. These mitigations highlight that while the risks are novel, many defenses extend from established best practices in security engineering. What changes in AI is the context and the stakes: familiar safeguards must be adapted to cover prompt-driven workflows, model assets, and plugin ecosystems. Mitigation alignment ensures that the OWASP list is not just descriptive but prescriptive, pointing toward practical defenses that organizations can adopt immediately.
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Cross-mapping with traditional application security shows that the OWASP GenAI/LLM Top 10 is not an isolated invention but part of a continuum. Many risks, such as injection or insecure output handling, echo the classic OWASP Top 10 categories but require reinterpretation in AI contexts. Prompt injection mirrors SQL injection in spirit, though the payload is natural language rather than code. Data leakage recalls information disclosure but includes new vectors like model memorization. Supply chain vulnerabilities mirror open-source library risks but extend to datasets and pre-trained checkpoints. This overlap provides continuity of controls, reminding practitioners that established AppSec lessons still apply. The unique additions—like overreliance on output—show where AI diverges, demanding adaptations that traditional frameworks never considered.
Using the OWASP list during development embeds security awareness into the software lifecycle. At the design phase, teams can consider risks like plugin misuse or model theft before architectures are finalized. Testing pipelines can include checks for insecure output handling or excessive permissions in connectors. Threat modeling efforts can integrate the OWASP list directly, ensuring coverage of the most pressing risks. Training teams on the list familiarizes them with both terminology and mitigation practices, making security a shared language across engineers, data scientists, and managers. In this way, the Top 10 becomes not just a checklist but a developmental compass, guiding secure-by-design practices.
Governance also benefits from the OWASP Top 10 by linking technical risks to organizational policies. Defined risks like data leakage or insufficient governance map directly to acceptable use rules and compliance reporting. Executives can use the list to understand where resources must be allocated and how risks should be communicated at the board level. Compliance teams can align audit requirements with the risks described, embedding them into risk registers. By feeding the OWASP Top 10 into governance frameworks, organizations ensure that security risks are not treated as isolated technical issues but as enterprise-wide responsibilities. The list becomes a bridge between engineers and leaders, translating vulnerabilities into governance obligations.
Operational benefits emerge from adopting the OWASP list as a common reference. Scarce resources can be prioritized toward the most pressing risks, avoiding wasted effort on edge cases. A shared language simplifies communication across teams and industries, enabling benchmarking against peers. Industry-wide adoption means that best practices can be compared, shared, and improved collaboratively. Improved awareness across the organization reduces the chance that risks remain invisible until exploited. For AI, where many risks are still unfamiliar, the operational clarity provided by the OWASP list is invaluable. It elevates conversations from abstract warnings to actionable priorities.
The list, however, has limitations that must be acknowledged. It is not exhaustive: AI systems evolve rapidly, and new risks emerge that may not fit neatly into existing categories. The attack surface is expanding, and the list requires ongoing updates to remain relevant. Industry dependence also means that adoption may vary, with some sectors updating faster than others. Context-specific gaps may exist, where unique organizational or sector risks are not captured in the generic framework. These limitations remind organizations not to treat the list as a finished product but as a living reference. It is a starting point for security, not the final word.
The strategic value of the OWASP GenAI/LLM Top 10 lies in its role as a shared industry baseline. It provides a roadmap for improvement, giving organizations a structured way to address security incrementally. It also functions as a communication tool, enabling security teams to explain complex issues in language recognized across industries. For stakeholders, the list builds trust by showing that risks are not being managed in isolation but are part of a global effort. By aligning with the list, organizations demonstrate both awareness and commitment, two qualities that strengthen credibility. Ultimately, the OWASP Top 10 is more than a catalog of risks—it is a trust-building framework for responsible AI security.
In conclusion, the OWASP GenAI/LLM Top 10 provides a structured awareness framework that distills the most critical security risks facing generative AI systems. Its purpose mirrors the classic OWASP Top 10: raising awareness, creating a shared language, and prioritizing defenses where they matter most. For AI, the list adapts familiar categories—such as injection, data leakage, and insecure outputs—while also introducing unique risks like overreliance on model responses. This balance ensures continuity with established AppSec practices while acknowledging the novel challenges that AI presents. The Top 10 is not exhaustive, but it provides an accessible starting point for teams at all levels of maturity.
The risks outlined in the Top 10 highlight that AI systems face both old and new challenges. Prompt injection, data leakage, and insecure outputs mirror problems long seen in traditional systems but amplified by AI’s scale and autonomy. Model denial of service and supply chain vulnerabilities reflect resource dependence and external reliance. Insecure plugin interfaces, inadequate monitoring, and model theft expose the fragility of integrations and intellectual property. Overreliance on outputs and insufficient governance demonstrate that technical controls alone are insufficient—cultural and organizational measures are equally essential. Together, these risks present a holistic view of where AI security is most fragile.
Mitigations aligned with the list ensure that risks are not only acknowledged but also addressed. Filtering inputs reduces injection risks, encryption protects sensitive data, sandboxing constrains outputs, and monitoring dashboards close visibility gaps. Governance frameworks, acceptable use policies, and strong key management address organizational weaknesses. By linking risks to mitigations, the OWASP list provides not only awareness but also a roadmap for action. Organizations can prioritize based on their own environments, scaling mitigations as resources allow. In this way, the list is not just diagnostic but prescriptive, guiding practical security improvements.
Integration with development lifecycles ensures that the OWASP Top 10 does not remain a static document. During design, the list shapes architectural choices. During testing, it informs validation pipelines. During deployment, it integrates with threat modeling and monitoring. In governance, it maps directly to policies, board reporting, and compliance efforts. This integration makes the Top 10 a living part of AI security, shaping both technical and organizational practices. Its greatest value lies in making security awareness continuous and embedded rather than occasional and reactive.
Strategically, the OWASP GenAI/LLM Top 10 builds trust across the AI ecosystem. It reassures clients and regulators that risks are not hidden but acknowledged and managed. It provides a baseline for industry collaboration, enabling peers to share practices and benchmark progress. It enables communication between technical specialists and executives, translating risk into language both understand. By anchoring conversations in a globally recognized framework, it creates consistency in a field otherwise prone to fragmentation. Trust, awareness, and prioritization are the ultimate strategic contributions of the list.
As we transition to the next episode on secure software development lifecycles, the connection is clear. The OWASP Top 10 identifies the risks most worth addressing, but the secure SDLC describes how to integrate those protections from design to deployment. Together, they provide both the map and the process: one highlighting the dangers, the other guiding the journey to safer AI systems. By mastering the Top 10, you are now prepared to see how these risks translate into actionable practices across the entire development pipeline, ensuring that security is not bolted on but built in from the very beginning.
